Bayesian networks in the management of hospital admissions: a comparison between explainable AI and black box AI during the pandemics.

How to cite: Nicora, G.; Catalano, M.; Bortolotto, C.; Achilli, M.F.; Messana, G.; Lo Tito, A.; Consonni, A.; Cutti, S.; Comotto, F.; Stella, G.M.; Corsico, A.G.; Perlini, S.; Bellazzi, R.; Bruno, R.; Preda, L. Bayesian networks in the management of hospital admissions: a comparison between explainable AI and black box AI during the pandemics.. Preprints 2024, 2024030647. https://doi.org/10.20944/preprints202403.0647.v1 Nicora, G.; Catalano, M.; Bortolotto, C.; Achilli, M.F.; Messana, G.; Lo Tito, A.; Consonni, A.; Cutti, S.; Comotto, F.; Stella, G.M.; Corsico, A.G.; Perlini, S.; Bellazzi, R.; Bruno, R.; Preda, L. Bayesian networks in the management of hospital admissions: a comparison between explainable AI and black box AI during the pandemics.. Preprints 2024, 2024030647. https://doi.org/10.20944/preprints202403.0647.v1

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MDPI and ACS Style

Nicora, G.; Catalano, M.; Bortolotto, C.; Achilli, M.F.; Messana, G.; Lo Tito, A.; Consonni, A.; Cutti, S.; Comotto, F.; Stella, G.M.; Corsico, A.G.; Perlini, S.; Bellazzi, R.; Bruno, R.; Preda, L. Bayesian networks in the management of hospital admissions: a comparison between explainable AI and black box AI during the pandemics.. Preprints 2024, 2024030647. https://doi.org/10.20944/preprints202403.0647.v1

APA Style

Nicora, G., Catalano, M., Bortolotto, C., Achilli, M.F., Messana, G., Lo Tito, A., Consonni, A., Cutti, S., Comotto, F., Stella, G.M., Corsico, A.G., Perlini, S., Bellazzi, R., Bruno, R., & Preda, L. (2024). Bayesian networks in the management of hospital admissions: a comparison between explainable AI and black box AI during the pandemics.. Preprints. https://doi.org/10.20944/preprints202403.0647.v1

Chicago/Turabian Style

Nicora, G., Raffaele Bruno and Lorenzo Preda. 2024 "Bayesian networks in the management of hospital admissions: a comparison between explainable AI and black box AI during the pandemics." Preprints. https://doi.org/10.20944/preprints202403.0647.v1

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) approaches that could learn from large data sources have been identified as useful tools to support clinicians in their decisional process; AI and ML implementations have had a rapid acceleration during the recent COVID-19 pandemic. However, many ML classifiers are “black box” to the final user, since their underlying reasoning process is often obscure. Additionally, the performance of such models suffer from poor generalization ability in presence of dataset shift. Here, we present a comparison between an explainable-by-design (“white box”) model (Bayesian Network (BN)) versus a black-box model (Random Forest), both studied with the aim to support clinicians of Policlinico San Matteo University Hospital in Pavia (Italy) during the triage of COVID-19 patients. Our aim is to evaluate whether the BN predictive performances are comparable with those of a widely used but less explainable ML model such as Random Forest and to test the generalization ability of the ML models across different waves of the pandemic.

Keywords

Artificial Intelligence; Explainability; Machine Learning; Random Forest; Bayesian Networks; COVID-19

Subject

Medicine and Pharmacology, Emergency Medicine

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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